Apple Silicon’s M-Series integrates powerful GPUs with a physically unified memory architecture, which makes it a promising platform for the growing shift toward local-first analytical data processing on consumer devices. However, its suitability for database workloads remains largely unexplored.

Developing a code-generating Database Management System requires tight profiling and performance-tuning iterations. However, existing profilers report results at instruction or function level, making it challenging to correlate them with constructs like query plan operators to derive actionable insights. In this demonstration, we show how to solve this issue, building on our previous work, Tailored Profiling. We introduce UmbraPerf, a novel profiling tool that combines profiling samples with metadata to map profiling results to a DBMS's internal abstractions, from query plan components down to generated code. Its interactive frontend visualizes data across multiple abstraction levels, enabling developers to narrow performance bottlenecks from operators down to individual instructions. We demonstrate its utility through two scenarios using our publicly available frontend.

Dataflow graphs are a popular abstraction for describing computation, used in many systems for high-level optimization. For execution, dataflow graphs are lowered and optimized through layers of program representations down to machine instructions. Unfortunately, performance profiling such systems is cumbersome, as today's profilers present results merely at instruction and function granularity. This obfuscates the connection between profiles and high-level constructs, such as operators and pipelines, making interpretation of profiles an exercise in puzzling and deduction. In this paper, we show how to profile compiling dataflow systems at higher abstraction levels. Our approach tracks the code generation process and aggregates profiling data to any abstraction level. This bridges the semantic gap to match the engineer's current information need and even creates a comprehensible way to report timing information within profiling data. We have evaluated this approach within our compiling DBMS Umbra, showing that the approach is generally applicable for compiling dataflow systems and can be implemented with high accuracy and reasonable overhead.

We propose a novel method to represent compressed bitmaps. Similarly to existing bitmap compression schemes, we exploit the compression potential of bitmaps populated with consecutive identical bits, i.e., 0-runs and 1-runs. But in contrast to prior work, our approach employs a binary tree structure to represent runs of various lengths. Leaf nodes in the upper tree levels thereby represent longer runs, and vice versa. The tree-based representation results in high compression ratios and enables efficient random access, which in turn allows for the fast intersection of bitmaps. Our experimental analysis with randomly generated bitmaps shows that our approach significantly improves over state-of-the-art compression techniques when bitmaps are dense and/or only barely clustered. Further, we evaluate our approach with real-world data sets, showing that our tree-encoded bitmaps can save up to one third of the space over existing techniques.